Remote determination of quantity stored in containers in geographical region

ABSTRACT

Disclosed is a method and system for processing images from an aerial imaging device. An image of an object of interest is received from the aerial imaging device. A parameter vector is extracted from the image. Image analysis is performed on the image to determine a height and a width of the object of interest. Idealized images of the object of interest are generated using the extracted parameter vector, the determined height, and the determined width of the object of interest. Each idealized image corresponds to a distinct filled volume of the object of interest. The received image of the object of interest is matched to each idealized image to determine a filled volume of the object of interest. Information corresponding to the determined filled volume of the object of interest is transmitted to a user device.

CROSS REFERENCE TO RELATED APPLICATION

This application is a Continuation of U.S. application Ser. No.15/470,563, filed Mar. 27, 2017, which claims the benefit of U.S.Provisional Application No. 62/320,387, filed Apr. 8, 2016, both ofwhich are incorporated by reference in their entirety.

TECHNICAL FIELD

This disclosure relates generally to image processing and, inparticular, to determining the quantity stored in remote objects in ageographical area using images captured by an aerial imaging device.

BACKGROUND

Several applications analyze aerial images to identify objects in theimages, for example, various objects in aerial images captured bysatellites. Analysis of high resolution images can be performed usingrelatively simple techniques. Obtaining high resolution aerial imagestypically requires use of large, expensive satellites and results. Thesesatellites typically require a significant amount of resources. Forexample, such satellites carry sophisticated and expensive equipmentsuch as high spatial resolution cameras, expensive transponders, andadvanced computers. Other factors that contribute to the cost associatedwith expensive imaging satellites are the launch cost and maintenance.Expensive high spatial resolution imaging satellites must be monitoredfrom a ground facility, which requires expensive manpower. Thesesatellites are also susceptible to damage or costly downtimes. The highlaunch and development costs of expensive imaging satellites leads to aslowdown in the introduction of new or upgraded satellite imagery andcommunication services for object detection.

Cheaper low spatial resolution imaging satellites may be used forcapturing images. However, such satellites and provide unclear images.In low-resolution imagery, objects such as containers or tanks aretypically not clearly identifiable and often appear as blobs containinga few adjacent pixels. In other instances, such as in infrared bandimagery, the images may be completely invisible to humans.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosed embodiments have advantages and features which will bemore readily apparent from the detailed description, the appendedclaims, and the accompanying figures (or drawings). A brief introductionof the figures is below.

FIG. 1 illustrates a block diagram of an example system environment inwhich a remote container analysis system operates, in accordance with anembodiment.

FIG. 2 illustrates a block diagram of an example system architecture forthe remote container analysis system, in accordance with an embodiment.

FIG. 3A illustrates an example positive training set for the remotecontainer analysis system, in accordance with an embodiment.

FIG. 3B illustrates an example negative training set for the remotecontainer analysis system, in accordance with an embodiment.

FIG. 4 illustrates an example process for training a machine learningmodel in the remote container analysis system, in accordance with anembodiment.

FIG. 5 illustrates an example process for the remote container analysissystem for identifying remote objects, in accordance with an embodiment.

FIG. 6 illustrates an example process for the remote container analysissystem for determining the filled volume of remote objects, inaccordance with an embodiment.

FIG. 7 illustrates an example synthesis of an idealized image, inaccordance with an embodiment.

FIG. 8 illustrates a set of example circle projection equations, inaccordance with an embodiment.

FIG. 9 illustrates a set of example idealized images, in accordance withan embodiment.

FIG. 10A illustrates an example received image of a container, inaccordance with an embodiment.

FIG. 10B illustrates an example image gradient for a received image, inaccordance with an embodiment.

FIG. 10C illustrates an example outline of the top rim of a container inan idealized image, in accordance with an embodiment.

FIG. 10D illustrates an example outline of a shadow on the inner surfaceof a container in an idealized image, in accordance with an embodiment.

FIG. 11 is a block diagram illustrating components of an example machineable to read instructions from a machine-readable medium and executethem in a processor or controller.

DETAILED DESCRIPTION

The Figures (FIGS.) and the following description relate to preferredembodiments by way of illustration only. It should be noted that fromthe following discussion, alternative embodiments of the structures andmethods disclosed herein will be readily recognized as viablealternatives that may be employed without departing from the principlesof what is claimed.

Reference will now be made in detail to several embodiments, examples ofwhich are illustrated in the accompanying figures. It is noted thatwherever practicable similar or like reference numbers may be used inthe figures and may indicate similar or like functionality. The figuresdepict embodiments of the disclosed system (or method) for purposes ofillustration only. One skilled in the art will readily recognize fromthe following description that alternative embodiments of the structuresand methods illustrated herein may be employed without departing fromthe principles described herein.

Configuration Overview

Disclosed by way of example embodiments are systems, methods and/orcomputer program products (e.g., a non-transitory computer readablestorage media that stores instructions executable by one or moreprocessing units) for identifying remote objects, such as cylindricalcontainers or tanks with floating roof structures over large geographicregions (e.g., a country), and determining the filled volume of remoteobjects.

In one example embodiment, a remote container analysis system mayreceive an image of an object of interest, such as a cylindricalcontainer or tank with a floating roof structure from an aerial imagingdevice, such as a satellite, drone, or other aerial configured imagingsystem. Such tanks are typically found in clusters or “tank farms.” Thesystem extracts a parameter vector from the image. The parameter vectormay include a parameter describing an elevation angle of the aerialimaging device. The system performs image analysis on the image todetermine a height and a width of the object of interest. The systemgenerates idealized image templates of the object of interest using theextracted parameter vector and the determined height and width of theobject of interest. Each idealized image corresponds to a distinctfilled volume of the object of interest, such as 30%, 70%, etc. Thesystem matches the received image of the object of interest to eachidealized image to determine the filled volume of the object of interestby performing a dot product between pixels of the received image andpixels of the idealized image. The system transmits informationcorresponding to the determined filled volume of the object of interestto a user device.

Example System Environment

Referring now to Figure (FIG. 1, it illustrates a block diagram of anexample system environment in which a remote container analysis system101 operates, in accordance with an embodiment. The example systemenvironment shown in FIG. 1 may include an aerial imaging device 110,the remote container analysis system 101, and a user device 120.

The aerial imaging device 110 shown in FIG. 1 may be a satellite, drone,or other aerial configured imaging system, capable of capturing lowresolution images. The images may correspond to the same spectral bandor different spectral bands, where a spectral band corresponds to arange of wavelengths of light. Example spectral bands include the redspectral band, the green spectral band, the blue spectral band, theinfrared spectral band, and the panchromatic spectral band. It is notedthat low resolution images have resolution significantly less (e.g., 15m per pixel) than high resolution images (e.g., 50 cm per pixel).

The remote container analysis system 101 may contain an image store 102,an optional feature extraction module 104, a machine learning model 106,a container analysis module 107, a parameter extraction module 105, anda template generation module 103. The image store 102 shown in FIG. 1may store images received from the aerial imaging device 110, asillustrated and described below with reference to FIG. 2. The featureextraction module 104 may optionally extract feature vectors from theimages received from the aerial imaging device 110. For example, afeature vector may include aggregate values based on pixel attributes ofpixels in the images, as illustrated and described below with referenceto FIG. 4. The remote container analysis system 101 transmits thefeature vector to the machine learning model 106 to identify objects ofinterest in the images as illustrated and described below with referenceto FIG. 5. The container analysis module 107 may analyze a patternrelated to the identified objects of interest in the images, forexample, times of capture of images, counts of the images, filledvolumes of the images.

The parameter extraction module 105 may extract parameters from theimage, for example, a parameter describing an azimuth angle of theaerial imaging device 110, a parameter describing an elevation angle ofthe sun, and a parameter describing an azimuth angle of the sun. Theparameters are used by the template generation module 103 to generateidealized image templates of the object of interest using the extractedparameters, as illustrated and described below with reference to FIG. 9.Each idealized image corresponds to a distinct filled volume of theobject of interest, for example, 35%.

The remote container analysis system 101 may interact with the userdevice 120 shown in FIG. 1. The user device 120 may be a computingdevice capable of receiving user input as well as transmitting and/orreceiving data via a network. In one example embodiment, a user device120 may be a conventional computer system, such as a desktop or laptopcomputer. Alternatively, a user device 120 may be a device havingcomputer functionality, such as a personal digital assistant (PDA), amobile telephone, a smartphone, a tablet, or another suitable device.The remote container analysis system 101 may transmit a visualrepresentation of the object of interest to the user device 120 oroutput a visual representation of the object of interest to a userinterface, for example, through graphical icons, graphical overlays, andother visual indicators.

Example System Architecture

Turning now to FIG. 2, it illustrates a block diagram of an examplesystem architecture for the remote container analysis system 101, inaccordance with an embodiment. The system architecture shown in FIG. 2may include an external system interface 201, the image store 102, theparameter extraction module 105, the template generation module 103, theoptional feature extraction module 104, an optional feature store 202,the machine learning model 106, a machine learning training engine 203,an image analysis module 204, a template matching module 205, thecontainer analysis module 107, and a container pattern store 206.

The external system interface 201 shown in FIG. 2 may be a dedicatedhardware or software networking device that receives data packetsrepresenting images from the aerial imaging device 110. The externalsystem interface 201 may forward data packets representing visualrepresentation of the objects of interest or information correspondingto the determined filled volume of the objects of interest from theremote container analysis system 101 via a network to user devices 120.In one example, the external system interface 201 forwards data packetsat high speed along the optical fiber lines of the Internet backbone. Inanother example, the external system interface 201 exchanges routinginformation using the Border Gateway Protocol (BGP) and may be an edgerouter, a border router, or a core router.

The image store 102 shown in FIG. 2 may store images received from theaerial imaging device 110. To make scanning over a large area practicaland efficient, lower resolution imagery is used for the first phase ofthe system. An example of such imagery is 15 m/pixel Landsat imagery (inthe panchromatic band). The first phase of the system is designed tohave high recall at the cost of lower precision. In a second phase,higher resolution imagery, e.g., 50 cm/pixel may be used to train themachine learning model 106 to filter out false alarms, as describedbelow with reference to FIG. 4. The parameter extraction module 105, theoptional feature extraction module 104, and the image analysis module204 may retrieve images stored by the image store 102 for processing.The image store 102 may be organized as a database or table of imagesstored on one or more of removable or non-removable memory cards, tapecassettes, zip cassettes, and computer hard drives. In one embodiment,the image store 102 may include multiple data fields, each describingone or more attributes of the images. In one example, the image store102 contains, for a single image, the time of capture, spectral bandinformation, geographical area coordinates, etc.

The optional feature extraction module 104 may extract feature vectorsfrom the images in the image store 102. The feature vector may includeaggregate values based on pixel attributes of pixels in the images. Inan embodiment, the feature extraction module 104 may optionally identifyclusters of adjacent pixels using pixel clustering. Within an identifiedcluster, adjacent pixels may match each other based on a pixelattribute. For example, for a grayscale image, the pixel attribute maybe a single number that represents the brightness of the pixel. In thisexample, the pixel attribute is a byte stored as an 8-bit integer givinga range of possible values from 0 to 255. Zero represents black and 255represents white. Values in between 0 and 255 make up the differentshades of gray. In another example of color images, separate red, greenand blue components are specified for each pixel. In this example, thepixel attribute is a vector of three numbers.

The optional feature extraction module 104 shown in FIG. 2 may identifypixel clusters in the images from the image store 102 by initializingeach pixel in an image as a region with the attribute of the pixel. Thefeature extraction module 104 identifies two adjacent regions having themost similar attribute value. These two regions are merged to form a newregion containing all the pixels of the two regions and having theattribute value as the average of the attribute values of the tworegions. The feature extraction module 104 repeats the process untilthere are no similar regions left.

Other embodiments of the feature extraction module 104 shown in FIG. 2may use one or a combination of the following: (a) edge/corner detectionmethods, such as Harris Corner or Canny edge, which find edges orcorners in the image to use as candidate features; (b) image gradients,which extract edge strength information; (c) oriented filters, whichidentify specific shapes; (d) thresholding methods, which use local orglobal threshold values to extract features; (e) image patch descriptorssuch as Scale-Invariant Feature Transform (SIFT), Speeded Up RobustFeatures (SURF), Features from Accelerated Segment Test (FAST), BinaryRobust Independent Elementary Features (BRIEF), Fast Retina Keypoint(FREAK), and Histogram of Oriented Gradients (HOG), which calculateorientation and edge description features at a given image patch.

The feature extraction module 104 shown in FIG. 2 may perform edgeanalysis in the received images to identify pixels in imagescorresponding to the object of interest. The feature extraction module104 may operate on each pixel location (i, j) in an image. Here, irepresents the row value of a pixel location in the image and jrepresents the column value of the pixel in the image. In one exampleembodiment, S represents an image and M represents the correspondingobject map image output. The function M(i, j) is defined to be 1whenever location (i, j) in image S corresponds to an object pixel and 0otherwise. The feature extraction module 104 may identify points in animage at which the pixel attributes change sharply. The points at whichpixel attributes change sharply may be organized into a set of curvedline segments termed edges. The feature extraction module 104 mayperform three steps in the edge analysis process to identify pairs ofedges: filtering, enhancement, and detection. The filtering step reducesnoise, for example, salt and pepper noise, impulse noise and Gaussiannoise in the images. The enhancement emphasizes pixels at locations (i,j) where there is a significant change in the pixel attribute value. Inone example, the feature extraction module 104 performs enhancement bycomputing the gradient magnitude of the image at various pixel locations(i, j). The detection searches for pixel locations (i, j) that have agradient value higher than a threshold to detect edge pixels.

In alternative embodiments, the feature extraction module 104 shown inFIG. 2 may analyze an image to create a probabilistic heat map orblocked image containing an area of interest where objects of interestare expected. The feature extraction module 104 may be furtherconfigured to incorporate other mapping sources, which contain geometricinformation (e.g., points, lines, and polygons). The feature extractionmodule 104 may use the geometric information to directly create theprobabilistic heat map or in conjunction with other image processingoperations, for example, as a line finding algorithm or random forestalgorithm, or using machine learning methods such as Support VectorMachines (SVM), neural network, or convolutional neural network (CNN),which requires no feature extraction.

Referring back to FIG. 2, the feature extraction module 104 reduces theredundancy in images, e.g., repetitive pixel values, to transform animage into a reduced set of features (features vector). The featurevector contains the relevant information from the images, such thatobjects of interest can be identified by the machine learning model 106by using this reduced representation instead of the complete initialimage. Example features extracted by the feature extraction module 104are illustrated and described in FIG. 4. In some example embodiments,the following dimensionality reduction techniques may be used by thefeature extraction module 104: independent component analysis, Isomap,Kernel PCA, latent semantic analysis, partial least squares, principalcomponent analysis, multifactor dimensionality reduction, nonlineardimensionality reduction, Multilinear Principal Component Analysis,multilinear subspace learning, semidefinite embedding, Autoencoder, anddeep feature synthesis.

The feature store 202 shown in FIG. 2 stores features extracted fromreceived images by the feature extraction module 104. The remotecontainer analysis system 101 retrieves the stored features for trainingthe machine learning model 106. The feature store 202 may be organizedas a database or table of images stored on one or more of removable ornon-removable memory cards, tape cassettes, zip cassettes, and computerhard drives.

The remote container analysis system 101 may train the machine learningmodel 106 using training sets and data from the feature store 202. Inone example embodiment, the machine learning model 106 may receivetraining sets including labeled clusters of pixels corresponding toobjects of interest, as illustrated and described below with referenceto FIGS. 3A and 3B. The machine learning training engine 203 shown inFIG. 2 may train the machine learning model 106 using training sets todetermine scores for clusters of pixels. The score is indicative of alikelihood that the clusters corresponds to objects of interest based onthe feature vector. The process followed by the machine learningtraining engine 203 is illustrated in FIG. 4. The remote containeranalysis system 101 may select clusters based on whether the scoreexceeds a threshold and associates the clusters with objects ofinterest.

In alternative example embodiments, the machine learning model 106 shownin FIG. 2 (in the form of a convolutional neural network) may generatean output, without the need for feature extraction, directly from theimages. A CNN is a type of feed-forward artificial neural network inwhich the connectivity pattern between its neurons is inspired by theorganization of a visual cortex. Individual cortical neurons respond tostimuli in a restricted region of space known as the receptive field.The receptive fields of different neurons partially overlap such thatthey tile the visual field. The response of an individual neuron tostimuli within its receptive field can be approximated mathematically bya convolution operation. CNNs are based on biological processes and arevariations of multilayer perceptrons designed to use minimal amounts ofpreprocessing. Advantages of CNNs include the obviation of featureextraction and the use of shared weight in convolutional layers, whichmeans that the same filter (weights bank) is used for each pixel in thelayer; this both reduces memory footprint and improves performance.

The machine learning model 106 may be a CNN that consists of bothconvolutional layers and max pooling layers. The architecture of themachine learning model 106 may be “fully convolutional,” which meansthat variable sized input images can be fed into it. The input to themachine learning model 106 may be a panchromatic Landsat image, and theoutput of the machine learning model 106 may be a per-pixel probabilitymap (i.e., for each pixel in the input image, the machine learning model106 considers a patch around that pixel and returns the probability thatthat pixel is part of a tank farm). All but the last convolutional layerin the machine learning model 106 may be followed by in-place rectifiedlinear unit activation. For all convolutional layers, the machinelearning model 106 may specify the kernel size, the stride of theconvolution, and the amount of zero padding applied to the input of thatlayer. For the pooling layers the model 106 may specify the kernel sizeand stride of the pooling.

The output of the machine learning model 106 (in the form of a CNN) mayoptionally include pixel clusters, where each pixel cluster includes oneor more adjacent pixels in a distinct image of the images, where theadjacent pixels match each other based on a pixel attribute. The outputmay include a score indicative of a likelihood that the pixel clusterscorrespond to an object of interest. The output may include one or morepixels locations corresponding to an object of interest. The output mayinclude the number of pixels in each an object of interest. The outputmay include an association between the pixel clusters and objects ofinterest.

The parameter extraction module 105 shown in FIG. 2 may extract aparameter vector from metadata in an image received from the aerialimaging device 110. The parameter vector may include example parametersdescribing the elevation angle of the aerial imaging device 110. Thesatellite elevation angle refers to the angle between a line pointingdirectly towards the satellite and the local horizontal plane. Aparameter may describe the time of capture of the received image. Aparameter may describe the azimuth angle of the aerial imaging device110. The azimuth angle is an angular measurement in a sphericalcoordinate system, which refers to the angle between the line pointingdirectly towards the satellite and a reference vector pointing North onthe reference plane.

A parameter extracted by the parameter extraction module 105 maydescribe the elevation angle of the sun. The elevation angle of the sunrefers to the angle between a line pointing directly towards the sun andthe local horizontal plane. A parameter may describe the azimuth angleof the sun. The azimuth angle of the sun refers to the angle between theline pointing directly towards the sun and a reference vector pointingNorth on the reference plane. A parameter may describe the geographicallocation of the center of the bottom of an object of interest in theimage. The remote container analysis system 101 operates under theassumption that some parameters may be inaccurate. Specifically, thesystem assumes that the location of the object and the satellite anglesmay not be accurate, but may be processed as described herein.

The image analysis module 204 retrieves images from the image store 102.The image analysis module 204 may perform image analysis on an image todetermine a height and a width of an object of interest in the image.For example, the image analysis module 204 may receive a pixelresolution r of the image of the object of interest. The image analysismodule 204 may determine a number h of pixels associated with the heightof the object of interest. The image analysis module 204 may determinethe height of the object of interest based on the pixel resolution r andthe number h of pixels associated with the height of the object ofinterest as height=r×h. The image analysis module 204 may determine thenumber of pixels w associated with the width of the object of interest.The image analysis module 204 may determine the width of the object ofinterest based on the pixel resolution r and the number of pixels wassociated with the width of the object of interest as width=r×w.

The image analysis module 204 may crop the received image to positionthe center of the object of interest in the center of the receivedimage. In embodiments, the image analysis module 204 may automaticallyremove the outer parts of the image to improve framing, accentuate theobject of interest, or change the aspect ratio. The image analysismodule 204 may rescale the received image of the object of interest bysetting pixels corresponding to shadows and inner surfaces of the objectof interest to negative values, e.g., −1, and setting pixelscorresponding to the roof of the object of interest to positive values,e.g., +1.

The template generation module 103 shown in FIG. 2 uses the parametervector extracted by the parameter extraction module 105 and synthesizesidealized image templates based on trigonometric projection for thegeometry of the object of interest for different filled volumepercentages, e.g., 10%, 30%, etc. A set of templates is generated byvarying the filled volume percentage. FIG. 9 illustrates a set ofidealized images 900 for a cylindrical tank container corresponding todifferent filled volume percentages. The template generation module 103generates the idealized images of the object of interest using theextracted parameter vector, the determined height, and the determinedwidth of the object of interest. To allow for inaccuracies in thesatellite view angles (elevation and azimuth), the template generationmodule 103 may perform a sweep over a range of angle values around theangle values extracted by the feature extraction module 104.

The template generation module 103 assumes that the received image hasthe object of interest in the center, although some error in the preciselocation is allowed for by the synthesis process. The templategeneration module 103 also assumes that the object, including its roof,is light-colored. It assumes that shadows cast by the roof of the objectand its top rim, and the inner walls of the object are dark-colored.Idealized image templates are constructed from the position of circles,as illustrated and described below with reference to FIG. 7. The circlescorrespond to the top rim of the object of interest, the bottom of theobject of interest, the arc of the shadow on the inner surface of theobject of interest, and the roof of the object of interest. Inembodiments illustrated in FIG. 8, the idealized images may beconstructed from only the position of the circles corresponding to thetop rim of the object of interest, the arc of the shadow on the innersurface of the object of interest, and the roof of the object ofinterest. The template generation module 103 uses the object's heightand width, desired floating-roof height, the two satellite angles andthe two sun angles. Using that information and the trigonometricequations shown in FIG. 8, the template generation module 103 creates 2Dprojections of where the circles lie. The template generation module 103may also crop each idealized image to position the center of the objectof interest in the center of each idealized image.

Once the circle positions are generated, the template generation module103 synthesizes the idealized images illustrated in FIG. 9 for differentfilled volumes of the object by performing a convolution on the circlecorresponding to the top rim, the circle corresponding to the arc of theshadow, and the circle corresponding to the roof. The templategeneration module 103 performs the convolution by performing unions andintersections between the three circles to generate the “eyeball” shapes(dark and shadow regions) for the idealized object images shown in FIG.9. The template generation module 103 may rescale each idealized imageof the object of interest by setting pixels corresponding to shadows andinner surfaces of the object of interest to negative values such as −1,setting pixels corresponding to the roof of the object of interest topositive values such as +1, and setting all other pixels to 0.

Unions and intersections between the three circles may be performed bythe template generation module 103, e.g., using morphological imageprocessing. Morphological image processing refers to non-linearoperations related to the shape or morphology of features in an image.Morphological image processing operations rely only on the relativeordering of pixel values, not on their numerical values, and thereforeare suited to the rescaled idealized images. The intersection of twoimages A and B, written A∩B, is the binary image which is 1 at allpixels p which are 1 in both A and B. The union of A and B, written A∪Bis the binary image which is 1 at all pixels p which are 1 in A or 1 inB (or in both).

The template matching module 205 shown in FIG. 2 matches the receivedimage of the object of interest to each idealized image synthesized bythe template generation module 103 to determine a filled volume of theobject of interest. The matching may be performed by performing a dotproduct between pixels of the received image and pixels of the idealizedimage. Because the received image and the idealized image are rescaledsuch that its pixel values range from −1 to +1, i.e., dark pixels(shadows, inner wall, etc.) are negative and light pixels (roof, etc.)are positive, performing a dot product between the received image andthe idealized image results in a large positive number if the receivedimage and the idealized image look similar. This is because positivepixels in the received image line up with positive pixels in theidealized image, and negative pixels in the received image line up withnegative pixels in the idealized image.

Performing the dot product by the template matching module 205 shown inFIG. 2 is an algebraic operation that takes pixels of the received imageand pixels of the idealized image and returns a single number.Algebraically, the dot product is the sum of the products of thecorresponding pixel values of the pixels of the received image andpixels of the idealized image. For example, the dot product of the twoimages A=[a₁, a₂, . . . , a_(n)] and B=[b₁, b₂, . . . , b_(n)], where Ais the received image and B is the idealized image template may bedetermined as A·B=Σ_(i)a_(i)b_(i)=a₁b₁+a₂b₂+ . . . +a_(n)b_(n). Furtherdetails of the convolutions performed by the image analysis module 204and the template matching module 205 to avoid false positive matches areillustrated and described below with reference to FIG. 10A.

To allow for inaccuracies in geo-referenced imagery, and the fact thatan object may not be precisely in the location expected, the templatematching module 205 performs a sweep over the received image to accountfor a number of possible locations of the object. The template matchingmodule 205 performs the sweep by using 2D convolution between thereceived image and each template. Once the template matching module 205has found a template match for the received image of the object ofinterest, it determines the filled volume of the object of interest asthe filled volume corresponding to the matching idealized imagetemplate.

The container analysis module 107 may analyze an object of interestpattern including one or more of the time of capture of the receivedimage, the count of one or more objects of interest in the receivedimage, and the determined filled volume of each of one or more objectsof interest in the received image. The container analysis module 107 maysend information to the user device 120 if the analyzed object ofinterest pattern exceeds a threshold. For example, the containeranalysis module 107 may send information to the user device 120 if thecount of the objects of interest in the received image exceeds athreshold or the determined filled volume of a threshold number ofobjects exceeds a threshold.

The container pattern store 206 shown in FIG. 2 may store patternsreceived from the container analysis module 107. The container patternstore 206 may be organized as a database or table stored on one or moreof removable or non-removable memory cards, tape cassettes, zipcassettes, and computer hard drives. In one embodiment, the containerpattern store 206 stores multiple data fields, each describing one ormore attributes of an object. In one example, the container patternstore 206 stores, for a single object, the time of capture of images,geographical region coordinates the height of the object, and/or thewidth of the object.

Example Machine Learning Training Sets

FIG. 3A illustrates an example positive training set 300 for the remotecontainer analysis system 101, in accordance with an embodiment. As partof the training of the machine learning model 106, the machine learningtraining engine 203 forms a training set of features and traininglabels, e.g., container 305, by identifying a positive training set offeatures that have been determined to have the property in question(presence of containers), and, in some embodiments, forms a negativetraining set of features that lack the property in question, asdescribed below in detail with reference to FIG. 3B. For example, eachtraining set may include labeled pixel clusters corresponding tocontainers, e.g., containers 303. To collect a training set, polygonsmay be marked around known tank farms around the world and downloadedLandsat 8 imagery may be intersected with these polygons. Randomlysampled imagery may also be collected for a set of negative examples(i.e., images that contain no oil tank farms). Once trained, the machinelearning model 106 may be run on all imagery in a region of interest(e.g., the United States). The final output of the remote containeranalysis system 101 is a set of areas of interest (geometry polygons)where the machine learning model 106 returned a high output score.

The positive training set 300 shown in FIG. 3A contains features thathave been determined to have the presence of containers. The positivetraining set 300 may include labeled pixel clusters corresponding tocontainer 305, containers 303, and container 304. The positive trainingset 300 also contains labels for background regions water 301 and land302. The example training set 300 may correspond to a port where theland 302 meets water 301. In the positive training set 300, container305 is in the area labeled water, while containers 303 and container 304are in the area labeled land.

FIG. 3B illustrates an example negative training set 350 for the remotecontainer analysis system 101, in accordance with an example embodiment.The negative training set 350 shown in FIG. 3B contains features thathave been determined to lack the presence of containers. The negativetraining set 350 includes a false positive cluster of pixels 354 that islocated partly in the water 351 and partly on the land 352. The negativetraining set 350 also includes a false positive cluster of pixels 353related to two intersecting clusters of pixels. Since two containerscannot intersect each other, these two intersecting clusters of pixelsare a false positive and labeled as such (353).

In some example embodiments, the training sets 300 and 350 may becreated by manually labeling pixel clusters that represent high scoresand pixel clusters that represent low scores. In other embodiments, themachine learning training engine 203 may extract training sets fromstored images obtained from the image store 102. For example, if astored image contains pixel clusters located on land, e.g., containers303, the machine learning training engine 203 may use the pixel clustersas a positive training set. If a stored image contains a pixel clusterlocated partly on land and partly on water, e.g., false positive 354,the machine learning training engine 203 may use the pixel cluster as anegative training set.

Example Machine Learning Training Process

Referring now to FIG. 4, it illustrates an example training process forthe machine learning training engine 203 for the machine learning model106 in the remote container analysis system 101. The process may use theimage analysis module 204, the feature extraction module 104, and themachine learning model 106. FIG. 4 and the other figures use likereference numerals to identify like elements. A letter after a referencenumeral, such as “410 a,” indicates that the text refers specifically tothe element having that particular reference numeral. A referencenumeral in the text without a following letter, such as “410,” refers toany or all of the elements in the figures bearing that referencenumeral, e.g., “410” in the text refer to reference numerals “410 a”and/or “410 b” in the figures.

The image analysis module 204 may perform edge analysis in the trainingimages 401 to identify pixels in the training images 401 correspondingto the objects of interest. The feature extraction module 104 shown inFIG. 4 extracts features 410 from the training images 401. The features410 corresponding to the training images 401 are used for training themachine learning model 106 based on training labels 402. In one exampleembodiment, a feature 410 a may represent aggregate values based onpixel attributes of pixels in the images 401. Extracting the feature 410a from the training images 401 may include performing pixel clusteringto identify clusters of adjacent pixels in the training images 401. Theadjacent pixels in the training images 401 match each other based on apixel attribute. An example feature 410 b may represent whether twoclusters of adjacent pixels in an image intersect each other; thisfeature teaches the machine learning model 106 that the two pixelclusters may not represent a container because containers cannotintersect.

An example feature 410 c may represent whether a cluster of pixels islocated partly on land and partly on water; this feature teaches themachine learning model 106 that the pixel cluster may not represent acontainer because containers cannot be located partly on land 302 andpartly on water 301. A feature 410 d may represent an associationbetween pixel locations and a pixel attribute. For example, the feature410 d may represent the brightness value of a pixel relative to pixelslocated on its right in an image; this feature teaches the machinelearning model 106 that the pixel may be part of a pixel clusterrepresenting a container because the pixel is brighter than surroundingpixels. A feature 410 e may represent the brightness of a pixel relativeto the average brightness of pixels located on the same row in an image;this feature teaches the machine learning model 106 that the pixel maybe part of an image blob representing a container because the pixel isbrighter (e.g., greater illumination) than surrounding pixels.

The machine learning training engine 203 may train the machine learningmodel 106 shown in FIG. 4 using the feature vector 410 and traininglabels 402. In one embodiment, the machine learning model 106 is therebyconfigured to determine a score for each pixel location in an image, thescore indicative of a likelihood that the pixel location corresponds toa container. In another embodiment, the machine learning model 106 isconfigured to determine a score for pixel clusters, the score indicativeof a likelihood that the pixel clusters correspond to containers. Inalternative embodiments, the machine learning model 106 is configured togenerate an output including pixel clusters and a score indicative of alikelihood that the pixel clusters correspond to containers. In anembodiment, the machine learning model 106 is configured to generate anoutput including one or more pixel locations corresponding to a pixelcluster and a score indicative of a likelihood that the pixel locationscorrespond to a pixel cluster. In an embodiment, the machine learningmodel 106 is configured to generate an output including a number ofpixels in each identified pixel cluster. In an embodiment, the machinelearning model 106 is configured to generate an output including anassociation between the identified pixel clusters and containers.

The machine learning model training engine 203 may apply machinelearning techniques to train the machine learning model 106 that whenapplied to features outputs indications of whether the features have anassociated property or properties, e.g., that when applied to featuresof received images outputs estimates of whether there are containerspresent, such as probabilities that the features have a particularBoolean property, or an estimated value of a scalar property. Themachine learning training engine 203 may apply dimensionality reduction(e.g., via linear discriminant analysis (LDA), principle componentanalysis (PCA), or the like) to reduce the amount of data in the featurevector 410 to a smaller, more representative set of data.

The machine learning training engine 203 may use supervised machinelearning to train the machine learning model 106 shown in FIG. 4, withthe feature vectors 410 of the positive training set 300 and thenegative training set 350 serving as the inputs. In other embodiments,different machine learning techniques, such as linear support vectormachine (linear SVM), boosting for other algorithms (e.g., AdaBoost),logistic regression, naïve Bayes, memory-based learning, random forests,bagged trees, decision trees, boosted trees, boosted stumps, neuralnetworks, CNNs, etc., may be used. The machine learning model 106, whenapplied to the feature vector 410 extracted from a set of receivedimages, outputs an indication of whether a pixel cluster has theproperty in question, such as a Boolean yes/no estimate, or a scalarvalue representing a probability.

In some example embodiments, a validation set is formed of additionalfeatures, other than those in the training sets, which have already beendetermined to have or to lack the property in question. The machinelearning training engine 203 applies the trained machine learning model106 shown in FIG. 4 to the features of the validation set to quantifythe accuracy of the machine learning model 106. Common metrics appliedin accuracy measurement include: Precision=TP/(TP+FP) andRecall=TP/(TP+FN), where Precision is how many the machine learningmodel 106 correctly predicted (TP or true positives) out of the total itpredicted (TP+FP or false positives), and Recall is how many the machinelearning model 106 correctly predicted (TP) out of the total number offeatures that did have the property in question (TP+FN or falsenegatives). The F score (F-score=2×PR/(P+R)) unifies Precision andRecall into a single measure. In one embodiment, the machine learningtraining engine 203 iteratively re-trains the machine learning model 106until the occurrence of a stopping condition, such as the accuracymeasurement indication that the machine learning model 106 issufficiently accurate, or a number of training rounds having takenplace.

In alternative embodiments, the machine learning model 106 may be a CNNthat learns useful representations (features) such as which pixelclusters correspond to containers directly from training sets withoutexplicit feature extraction. For example, the machine learning model 106may be an end-to-end recognition system (a non-linear map) that takesraw pixels from the training images 401 directly to internal labels. Themachine learning model 106 shown in FIG. 4 (in the form of a CNN) maygenerate an output directly from the training images 401, without theneed for feature extraction, edge analysis or pixel clusteridentification.

Example Process for Identifying Remote Objects

FIG. 5 illustrates an example process for the remote container analysissystem 101 for identifying remote objects, in accordance with anembodiment. In some example embodiments, the process may have differentand/or additional steps than those described in conjunction with FIG. 5.Steps of the process may be performed in different orders than the orderdescribed in conjunction with FIG. 5. Some steps may be executed inparallel. Alternatively, some of the steps may be executed in paralleland some steps executed sequentially. Alternatively, some steps mayexecute in a pipelined fashion such that execution of a step is startedbefore the execution of a previous step.

The remote container analysis system 101 receives 500 a first image of ageographical area, where the first image has a first resolution. Thefirst image is of a large geographic region. The large geographic regionmay be predefined, for example, based on area. This area may be, forexample, an entire country, e.g., the United States, or a smallerportion such as a state/province or city, e.g., Texas or Houston. Tomake scanning over a large area practical and efficient, lowerresolution imagery is used for the first image. An example of suchimagery is 15 m/pixel Landsat imagery (in the panchromatic band). Thefeature extraction module 104 extracts 504 a first feature vector fromthe first image. The first feature vector may include aggregate valuesbased on pixel attributes of pixels in the first image, as describedabove with reference to FIG. 2. The remote container analysis system 101transmits 508 the first feature vector to the machine learning model 106to identify an area of interest containing an object of interest in thefirst image. Identifying the area of interest containing the object ofinterest in the first image includes, for each pixel in the first image,determining a likelihood that the pixel corresponds to the object ofinterest, as described above with reference to FIGS. 2 and 4. Themachine learning model 106 is trained to have high Recall at the priceof lower Precision (e.g., a higher false positive rate).

The remote container analysis system 101 receives 512 a second image ofthe geographical area. The second image has a second resolution higherthan the first resolution. The processing of the low resolution firstimage is followed by a cleanup phase on the second image. To filter outthe false positives, a second pass is performed over all areas ofinterest returned by the first pass. This time higher resolution imageryis used where individual containers can be seen more clearly (e.g.,using 50 cm per pixel imagery). The feature extraction module 104extracts 516 a second feature vector from the second image. The secondfeature vector includes aggregate values based on pixel attributes ofpixels in the area of interest, as described above with reference toFIG. 2.

The remote container analysis system 101 transmits 520 the secondfeature vector to the machine learning model 106 to determine alikelihood that the area of interest contains the object of interest.Determining the likelihood that the area of interest contains the objectof interest includes, for each pixel in the area of interest,determining a likelihood that the pixel corresponds to the object ofinterest, as described above with reference to FIGS. 2 and 4. If thelikelihood is below a threshold, the remote container analysis system101 trains 524 the machine learning model to filter out featurescorresponding to the area of interest in images having the firstresolution. To improve the accuracy of the machine learning model 106 aprocedure may be performed that is referred to as “bootstrapping” or“hard negative mining.” The clean-up is restricted to a reasonably smallset of high scoring areas of interest. Areas of interest receiving ahigh score but containing no objects are added back into the negativetraining sets, and the machine learning model 106 is trained again. Thisprocedure ensures that the training set contains “difficult” negativeexamples and can improve precision and reduce the number of falsepositives.

In one example embodiment, training 524 the machine learning model 106to filter out the features corresponding to the area of interestincludes extracting a feature vector corresponding to the area ofinterest from the first image. The remote container analysis system 101creates a training set including the feature vector and a labelcorresponding to a lack of objects of interest in the first image. Theremote container analysis system 101 configures the machine learningmodel 106, based on the training set, to identify the lack of objects ofinterest in the first image. In another example embodiment, training 524the machine learning model 106 to filter out the features correspondingto the area of interest includes extracting a feature vectorcorresponding to the area of interest from the first image andconfiguring the machine learning model 106, based on the extractedfeature vector, to report a lack of objects of interest in the firstimage.

If the likelihood that the area of interest in the second image containsthe object of interest exceeds a threshold, the remote containeranalysis system 101 transmits 528 a visual representation of the objectof interest to a user device, as described in FIG. 2.

Example Process for Determining the Filled Volume of Remote Objects

FIG. 6 illustrates an example process for the remote container analysissystem 101 for determining the filled volume of remote objects, inaccordance with an embodiment. In some embodiments, the process may havedifferent and/or additional steps than those described in conjunctionwith FIG. 6. Steps of the process may be performed in different ordersthan the order described in conjunction with FIG. 6. Some steps may beexecuted in parallel. Alternatively, some of the steps may be executedin parallel and some steps executed sequentially. Alternatively, somesteps may execute in a pipelined fashion such that execution of a stepis started before the execution of a previous step.

The remote container analysis system 101 processes satellite imagery tosearch for intersections of imagery and known floating-roof containerlocations. The container image is received 600 and cropped such that thecenter of the container is in the center of the image. Using the croppedimage, the task is to determine the filled volume of the container(i.e., determine how far down the roof is). In an example embodiment,the system is configured so that the containers are assumed to be lightcolored, and the inner walls of each container are dark colored. Theremote container analysis system 101 extracts 604 a parameter vectorfrom the image. The parameter vector may include parameters describingthe latitude and longitude of the container, an image timestamp, thesatellite elevation and azimuth angles, the sun elevation and azimuthangles, and the tank height and width (or diameter).

In an example embodiment, the remote container analysis system 101 mayperform 608 image analysis on the image to determine the height andwidth of the object of interest (container), as described above withreference to FIG. 2. The remote container analysis system 101 generates612 idealized images of the object of interest using the extractedparameter vector, the determined height, and the determined width of theobject of interest, as described above with reference to FIG. 2 andillustrated below with reference to FIG. 7. Each idealized imagecorresponds to a distinct filled volume of the object of interest, asillustrated and described below with reference to FIG. 9.

The remote container analysis system 101 matches 616 the received imageof the object of interest to each idealized image to determine thefilled volume of the object of interest. The matching includesperforming a dot product between pixels of the received image and pixelsof the idealized image, as described above with reference to FIG. 2 andfurther illustrated below with reference to FIG. 9. The remote containeranalysis system 101 transmits 620 information corresponding to thedetermined filled volume of the object of interest to a user device 120,as described above with reference to FIG. 2.

Example Synthesis of Idealized Images

FIG. 7 illustrates an example synthesis 700 of an idealized image, inaccordance with an embodiment. The template generation module 103assumes that the container, including its roof, is white orlight-colored. It also assumes that shadows and the inner containersurfaces are black. The template generation module 103 generatesidealized image templates from the positions of circles: a top circle704 (the top rim of the object), a bottom circle 720 (the bottom of theobject, where it contacts the ground), a roof height circle 708 (thatrepresents the roof of the object), and an internal shadow circle(generated from the arc of the internal shadow on the inner surface 712of the object). In embodiments, only the top circle 704, roof heightcircle 708, and internal shadow circle 712 may be used. To generate theidealized image templates, the template generation module 103 uses thefollowing information: object height and width, desired roof height, thetwo satellite angles, and the two sun angles. Based on the informationabove and the trigonometric equations shown in FIG. 8, the templategeneration module 103 creates 2D projections of where the circles lie.

The template generation module 103 generates an idealized image for agiven filled volume of the object by generating the circle 704corresponding to the top rim of the object of interest using theparameter vector as shown in FIG. 8. The template generation module 103generates a circle corresponding to an arc of a shadow on an innersurface 712 of the object of interest using the parameter vector. Thetemplate generation module 103 generates the circle 708 corresponding tothe roof of the object of interest using the parameter vector. Thetemplate generation module 103 uses the shadow 716 on the roof to createa template corresponding to the desired roof height as shown in FIG. 8.The template generation module 103 may synthesize the idealized image byperforming a convolution on the circle 704, the circle 720, the circlecorresponding to the arc of the internal shadow 712, and the circle 708.

Once the circle positions are known, the template generation module 103computes unions and intersections to generate the “eyeball” shape (darkand shadow regions) template shown in FIG. 7, as described above withreference to FIG. 2. In the final templates, internal shadow pixels andinterior wall pixels are set to −1, roof pixels are set to +1, and allother pixels are set to 0. This is done so that dark pixels (e.g.,shadows and inner surfaces) are negative and light pixels (e.g., roof)are positive. A dot product performed between the input image and anidealized image will then result in a large positive number if thetemplate and image are similar because positive pixels in the image willline up with positive pixels in the idealized image, and negative pixelsin the image will line up with negative pixels in the idealized image.

Example Circle Projection Equations

FIG. 8 illustrates a set of example circle projection equations, inaccordance with an embodiment. The template generation module 103generates idealized image templates from the positions of the circlesillustrated and described above with reference to FIG. 7 based on theextracted parameters and the trigonometric equations shown in FIG. 8.

In one embodiment, the template generation module 103 may createprojections, based on the trigonometric equations shown in FIG. 8 to mapthe parameter vector onto the circles. The template generation module103 may project the shadows cast by the top rim onto the roof and theinner surface onto a plane as follows. The projection of a point is itsshadow on the plane. The shadow of a point on the plane is the pointitself. For example, the projection from a point onto a plane may beperformed as follows. If C is a point, called the center of projection,then the projection of a point P different from C onto a plane that doesnot contain C is the intersection of the line CP with the plane. Thepoints P, such that the line CP is parallel to the plane do not have anyimage by the projection. However, they are regarded as projecting to apoint at infinity of the plane. The projection of the point C itself isnot defined. In another example, the projection may be performedparallel to a direction D, onto a plane as follows. The image of a pointP is the intersection with the plane of the line parallel to D passingthrough P.

In alternative embodiments, the template generation module 103 maydefine a projective space P(V) of dimension n over a field K as the setof the lines in a K-vector space of dimension n+1. If a basis of V hasbeen fixed, a point of V may be represented by a point (x₀, . . . ,x_(n)) of K^(n+1). A point of P(V), being a line in V, may thus berepresented by the coordinates of any nonzero point of this line. Giventwo projective spaces P(V) and P(W) of the same dimension, the templategeneration module 103 may generate an homography as a mapping from P(V)to P(W), which is induced by an isomorphism of vector spaces f:V→W. Suchan isomorphism induces a bijection from P(V) to P(W), because of thelinearity off. Two such isomorphisms, f and g, may define the samehomography if and only if there is a nonzero element a of K such thatg=af.

Example Idealized Images

FIG. 9 illustrates a set of example idealized images 900, in accordancewith an embodiment. The idealized images 900 are generated by thetemplate generation module 103 by varying the filled volume percentageof the container of interest from 0% filled (image 904) to 100% filled(image 924). In image 908, the filled volume percentage of the containeris 20%. In image 912, the filled volume percentage of the container is40%. The shadow 936 in image 912 cast by the top rim of the container onthe roof 932 and the inner surface of the container is smaller than theshadow in image 908.

In image 916, the filled volume percentage of the container is 60%. Theshadow in image 916 cast by the top rim of the container on the roof andthe inner surface of the container is smaller than the shadow 936 inimage 912. In image 920, the filled volume percentage of the containeris 80%. The shadow in image 920 cast by the top rim of the container onthe roof and the inner surface of the container is smaller than theshadow in image 916. In image 924, the filled volume percentage of thecontainer is 100%. There is no shadow in image 924.

For a given set of inputs, the remote container analysis system 101determines which idealized template among the images 900 matches thereceived image best, and then returns the corresponding filled volumepercentage. In one example embodiment, the template matching module 205determines the filled volume of the container based on the receivedimage, the satellite and sun angles, and the container dimensions asfollows. The template matching module 205 sets the variable “best_score”to a large negative number. The template matching module 205 sets thevariable “best_fill_percentage” to −1. The template matching module 205performs the following steps for different filled volume percentagesfrom 0% to 100%. The template matching module 205 determines the scorefrom matching the received image to each template. If the score ishigher than “best_score,” the template matching module 205 sets thevalue of “best_score” to the score and the value of“best_fill_percentage” to the filled volume percentage. At the end ofthe process, the template matching module 205 returns the value of“best_fill_percentage.”

Example Image Gradients and Outlines of Remote Objects

Referring now to FIG. 10A, it illustrates an example received image 1000of a container, in accordance with an embodiment. The container has aroof 1008 having a shadow 1004. When the roof 1008 of the floating-roofcontainer is all the way up (a full container), the matching idealizedimage is a white circle (where all the pixels have value 1) surroundedby gray pixels 1012 (pixels with a value of 0), illustrated above asimage 924 in FIG. 9. This template will match any white region with thesame score. To avoid false positives, gradient information from thereceived image 1000 may be incorporated by the template matching module205.

FIG. 10B illustrates an example image gradient 1020 for the receivedimage 1000 of FIG. 10A, in accordance with an embodiment. The imageanalysis module 204 may perform edge analysis, as described above withreference to FIG. 2, on the received image 1000 to obtain the imagegradient 1020 of the received image 1000. The image gradient 1020represents the directional change in the intensity or color in the image1000. The image analysis module 204 may derive the image gradient 1020as a single value at each pixel. At each image point, the gradientdenotes the largest possible intensity increase. The edge 1024 in FIG.10B represents the change in the intensity or color in the image 1000from the background 1012 to the shadow 1004 in FIG. 10A. The edge 1028in FIG. 10B represents the change in the intensity or color in the image1000 from the shadow 1004 to the roof 1008 in FIG. 10A. The edge 1032 inFIG. 10B represents the change in the intensity or color in the image1000 from the roof 1008 to the background 1012 in FIG. 10A.

FIG. 10C illustrates an example outline 1040 of a top rim 1044 of theobject of interest (container) in an idealized image template, inaccordance with an embodiment. The image analysis module 204 may performedge analysis on the idealized image to obtain the outline 1040 of thetop rim 1044 of the container in the idealized image. For example, theimage analysis module 204 may perform edge thinning to remove theunwanted spurious points on the edge 1044 in the outline 1040. The imageanalysis module 204 may perform edge thinning after the idealized imagehas been filtered for noise (e.g., using median, Gaussian filters etc.),the edge operator has been applied (as described above with reference toFIG. 2) to detect the edge 1044, and after the edge 1044 has beensmoothed using an appropriate threshold value. This removes all theunwanted points and results in one-pixel-thick edge elements in anembodiment.

The template matching module 205 may perform a dot product betweenpixels of the image gradient 1020 and pixels of the outline 1040 of thetop rim 1044 in order to determine the filled volume of the container inthe received image 1000. The benefits and advantages of this process arethat sharp and thin edges lead to greater efficiency in templatematching. Using Hough transforms to detect arcs (of shadows) and circles(e.g., the top rim) results in greater accuracy.

FIG. 10D illustrates an example outline 1060 of the shadow on the innersurface of a container in an idealized image template, in accordancewith an embodiment. The image analysis module 204 may perform edgeanalysis on the idealized image to obtain the outline 1060 of the shadowon the inner surface of the container. In FIG. 10D, edge 1064 representsthe change in the intensity or color in the idealized image from thebackground to the shadow on the inner surface. Edge 1068 represents thechange in the intensity or color in the idealized image from the shadowon the inner surface to the roof. The template matching module 205 mayperform a dot product between pixels of the image gradient 1020 andpixels of the outline of the shadow 1060 in order to determine thefilled volume of the container in the received image 1000.

In some example embodiments, three convolutions may be performed andadded up to form the response map. The first convolution is between thereceived image 1000 and the idealized image template, e.g., image 912 inFIG. 9. The second convolution is between the image gradient 1020 andthe outline of the top rim 1040. The third convolution is between theimage gradient 1020 and the outline of the shadow 1060. The threeresulting response maps may be summed, and the location with the maximalresponse within a specified radius of the center of the image may bedetermined as the final template match score. The above procedure may begeneralized to any situation where the geometry of the object ofinterest is known and characterized by a small number of parameters,most of which are known. The unknown parameters can then be determinedby sweeping over possible values, generating templates, and matchingthem to the input image.

Example Machine Architecture

FIG. 11 is a block diagram illustrating components of an example machineable to read instructions described as processes herein from amachine-readable medium and execute them in at least one processor (orcontroller). Specifically, FIG. 11 shows a diagrammatic representationof a machine in the example form of a computer system 1100. The computersystem 1100 can be used to execute instructions 1124 (e.g., program codeor software) for causing the machine to perform any one or more of themethodologies (or processes) described herein. In alternativeembodiments, the machine operates as a standalone device or a connected(e.g., networked) device that connects to other machines. In a networkeddeployment, the machine may operate in the capacity of a server machineor a client machine in a server-client network environment, or as a peermachine in a peer-to-peer (or distributed) network environment. It isnoted the instructions correspond to the functionality of componentsand/or processes described herein, for example, with respect to FIGS. 1,2, and 4-6. The instructions also may correspond to the processesassociated with driving to the results shown in FIGS. 3A-3B, 7, 9, and10A-10D.

The machine may be a server computer, a client computer, a personalcomputer (PC), a tablet PC, a set-top box (STB), a smartphone, aninternet of things (IoT) appliance, a network router, switch or bridge,or any machine capable of executing instructions 1124 (sequential orotherwise) that specify actions to be taken by that machine. Further,while only a single machine is illustrated, the term “machine” shallalso be taken to include any collection of machines that individually orjointly execute instructions 1124 to perform any one or more of themethodologies discussed herein.

The example computer system 1100 includes one or more processing units(generally processor 1102). The processor 1102 is, for example, acentral processing unit (CPU), a graphics processing unit (GPU), adigital signal processor (DSP), a controller, a state machine, one ormore application specific integrated circuits (ASICs), one or moreradio-frequency integrated circuits (RFICs), or any combination ofthese. The computer system 1100 also includes a main memory 1104. Thecomputer system may include a storage unit 1116. The processor 1102,memory 1104 and the storage unit 1116 communicate via a bus 1108.

In addition, the computer system 1100 can include a static memory 1106,a display driver 1110 (e.g., to drive a plasma display panel (PDP), aliquid crystal display (LCD), or a projector). The computer system 1100may also include alphanumeric input device 1112 (e.g., a keyboard), acursor control device 1114 (e.g., a mouse, a trackball, a joystick, amotion sensor, or other pointing instrument), a signal generation device1118 (e.g., a speaker), and a network interface device 1120, which alsoare configured to communicate via the bus 1108.

The storage unit 1116 includes a machine-readable medium 1122 on whichis stored instructions 1124 (e.g., software) embodying any one or moreof the methodologies or functions described herein. The instructions1124 may also reside, completely or at least partially, within the mainmemory 1104 or within the processor 1102 (e.g., within a processor'scache memory) during execution thereof by the computer system 1100, themain memory 1104 and the processor 1102 also constitutingmachine-readable media. The instructions 1124 may be transmitted orreceived over a network 1126 via the network interface device 1120.

While machine-readable medium 1122 is shown in an example embodiment tobe a single medium, the term “machine-readable medium” should be takento include a single medium or multiple media (e.g., a centralized ordistributed database, or associated caches and servers) able to storethe instructions 1124. The term “machine-readable medium” shall also betaken to include any medium that is capable of storing instructions 1124for execution by the machine and that cause the machine to perform anyone or more of the methodologies disclosed herein. The term“machine-readable medium” includes, but not be limited to, datarepositories in the form of solid-state memories, optical media, andmagnetic media. It is noted that in some example embodiments, the corecomponents of the computer system may disregard components except forthe processor 1102, memory 1104, and bus 1108 and may in otherembodiments also include the storage unit 1116 and/or the networkinterface device 1120.

Additional Considerations

The remote container analysis system as disclosed provides benefits andadvantages that include the transformation of clusters of pixels into adigital representation of remote containers, and for each remotecontainer, the digital representation of the roof, inner surfaces, andthe filled volume of the remote container. Other advantages of thesystem include faster processing of the aerial images, less powerconsumption, lower latency in remote container detection, less datatransmitted over the network, etc.

Throughout this specification, plural instances may implementcomponents, operations, or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. Structures andfunctionality presented as separate components in example configurationsmay be implemented as a combined structure or component. Similarly,structures and functionality presented as a single component may beimplemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

Certain embodiments are described herein as including logic or a numberof components, modules, or mechanisms, for example, as illustrated anddescribed with FIGS. 1, 2, 4, 5, 6, and 11. Modules may constituteeither software modules (e.g., code embodied on a machine-readablemedium) or hardware modules. A hardware module is tangible unit capableof performing certain operations and may be configured or arranged in acertain manner. In example embodiments, one or more computer systems(e.g., a standalone, client or server computer system) or one or morehardware modules of a computer system (e.g., a processor or a group ofprocessors) may be configured by software (e.g., an application orapplication portion) as a hardware module that operates to performcertain operations as described herein.

In various embodiments, a hardware module may be implementedmechanically or electronically. For example, a hardware module mayinclude dedicated circuitry or logic that is permanently configured(e.g., as a special-purpose processor, such as a field programmable gatearray (FPGA) or an application-specific integrated circuit (ASIC)) toperform certain operations. A hardware module may also includeprogrammable logic or circuitry (e.g., as encompassed within ageneral-purpose processor or other programmable processor) that istemporarily configured by software to perform certain operations. Itwill be appreciated that the decision to implement a hardware modulemechanically, in dedicated and permanently configured circuitry, or intemporarily configured circuitry (e.g., configured by software) may bedriven by cost and time considerations.

The various operations of example methods described herein may beperformed, at least partially, by one or more processors, e.g.,processor 1102, that are temporarily configured (e.g., by software) orpermanently configured to perform the relevant operations. Whethertemporarily or permanently configured, such processors may constituteprocessor-implemented modules that operate to perform one or moreoperations or functions. The modules referred to herein may, in someexample embodiments, include processor-implemented modules.

The one or more processors may also operate to support performance ofthe relevant operations in a “cloud computing” environment or as a“software as a service” (SaaS). For example, at least some of theoperations may be performed by a group of computers (as examples ofmachines including processors), these operations being accessible via anetwork (e.g., the Internet) and via one or more appropriate interfaces(e.g., application program interfaces (APIs).)

The performance of certain of the operations may be distributed amongthe one or more processors, not only residing within a single machine,but deployed across a number of machines. In some example embodiments,the one or more processors or processor-implemented modules may belocated in a single geographic location (e.g., within a homeenvironment, an office environment, or a server farm). In other exampleembodiments, the one or more processors or processor-implemented modulesmay be distributed across a number of geographic locations.

Some portions of this specification are presented in terms of algorithmsor symbolic representations of operations on data stored as bits orbinary digital signals within a machine memory (e.g., a computermemory). These algorithms or symbolic representations are examples oftechniques used by those of ordinary skill in the data processing artsto convey the substance of their work to others skilled in the art. Asused herein, an “algorithm” is a self-consistent sequence of operationsor similar processing leading to a desired result. In this context,algorithms and operations involve physical manipulation of physicalquantities. Typically, but not necessarily, such quantities may take theform of electrical, magnetic, or optical signals capable of beingstored, accessed, transferred, combined, compared, or otherwisemanipulated by a machine. It is convenient at times, principally forreasons of common usage, to refer to such signals using words such as“data,” “content,” “bits,” “values,” “elements,” “symbols,”“characters,” “terms,” “numbers,” “numerals,” or the like. These words,however, are merely convenient labels and are to be associated withappropriate physical quantities.

Unless specifically stated otherwise, discussions herein using wordssuch as “processing,” “computing,” “calculating,” “determining,”“presenting,” “displaying,” or the like may refer to actions orprocesses of a machine (e.g., a computer) that manipulates or transformsdata represented as physical (e.g., electronic, magnetic, or optical)quantities within one or more memories (e.g., volatile memory,non-volatile memory, or a combination thereof), registers, or othermachine components that receive, store, transmit, or displayinformation.

As used herein any reference to “one embodiment” or “an embodiment”means that a particular element, feature, structure, or characteristicdescribed in connection with the embodiment is included in at least oneembodiment. The appearances of the phrase “in one embodiment” in variousplaces in the specification are not necessarily all referring to thesame embodiment.

Some embodiments may be described using the expression “coupled” and“connected” along with their derivatives. For example, some embodimentsmay be described using the term “coupled” to indicate that two or moreelements are in direct physical or electrical contact. The term“coupled,” however, may also mean that two or more elements are not indirect contact with each other, but yet still co-operate or interactwith each other. The embodiments are not limited in this context.

As used herein, the terms “comprises,” “comprising,” “includes,”“including,” “has,” “having” or any other variation thereof, areintended to cover a non-exclusive inclusion. For example, a process,method, article, or apparatus that includes a list of elements is notnecessarily limited to only those elements but may include otherelements not expressly listed or inherent to such process, method,article, or apparatus. Further, unless expressly stated to the contrary,“or” refers to an inclusive or and not to an exclusive or. For example,a condition A or B is satisfied by any one of the following: A is true(or present) and B is false (or not present), A is false (or notpresent) and B is true (or present), and both A and B are true (orpresent).

In addition, use of the “a” or “an” are employed to describe elementsand components of the embodiments herein. This is done merely forconvenience and to give a general sense of the claimed invention. Thisdescription should be read to include one or at least one and thesingular also includes the plural unless it is obvious that it is meantotherwise.

Upon reading this disclosure, those of skill in the art will appreciatestill additional alternative structural and functional designs for asystem and a process for identifying and determining the filled volumeof remote containers from low resolution imagery through the disclosedprinciples herein. Thus, while particular embodiments and applicationshave been illustrated and described, it is to be understood that thedisclosed embodiments are not limited to the precise construction andcomponents disclosed herein. Various modifications, changes andvariations, which will be apparent to those skilled in the art, may bemade in the arrangement, operation and details of the method andapparatus disclosed herein without departing from the spirit and scopedefined in the appended claims.

What is claimed is:
 1. A method for processing images from an aerialimaging device, comprising: extracting, from an image of an object ofinterest, parameters describing at least an azimuth angle of the aerialimaging device; generating, from the parameters, idealized images of theobject of interest comprising a top rim of the object of interest, anarc of a shadow on an inner surface of the object of interest, and aroof of the object of interest; matching the image of the object ofinterest to each idealized image to determine a filled volume of theobject of interest; and transmitting information corresponding to thedetermined filled volume of the object of interest to a user device. 2.The method of claim 1, wherein the parameters further describe anelevation angle of the sun, an azimuth angle of the sun, and anelevation angle of the aerial imaging device.
 3. The method of claim 1,wherein each idealized image corresponds to a distinct filled volume ofthe object of interest.
 4. The method of claim 1, wherein the matchingof the image of the object of interest to each idealized image comprisesperforming a dot product between pixels of the image of the object ofinterest and pixels of the idealized image.
 5. A method for processingimages from an aerial imaging device, the method comprising: extracting,from an image of an object of interest, parameters describing at leastan azimuth angle of the aerial imaging device; generating, from theparameters, idealized images of the object of interest, wherein eachidealized image corresponds to a distinct filled volume of the object ofinterest; matching the image of the object of interest to each idealizedimage to determine a filled volume of the object of interest; andtransmitting information corresponding to the determined filled volumeof the object of interest to a user device.
 6. The method of claim 5,wherein the parameters further describe an elevation angle of the sun,an azimuth angle of the sun, and an elevation angle of the aerialimaging device.
 7. The method of claim 5, wherein the generating of eachidealized image comprises: generating circles corresponding to a top rimof the object of interest, an arc of a shadow on an inner surface of theobject of interest, and a roof of the object of interest; and performingconvolutions on the circles.
 8. The method of claim 7, wherein theperforming of the convolution comprises performing unions andintersections between the circles.
 9. The method of claim 5, furthercomprising rescaling the image of the object of interest by: settingpixels corresponding to shadows and inner surfaces of the object ofinterest to negative values; and setting pixels corresponding to a roofof the object of interest to positive values.
 10. The method of claim 5,wherein the matching of the image of the object of interest to eachidealized image comprises: performing dot products between pixels of theimage of the object of interest and pixels of the idealized imagesuperimposed at different locations of the image of the object ofinterest; and determining a maximum value of the dot product across thedifferent locations.
 11. The method of claim 5, wherein the matching ofthe image of the object of interest to each idealized image comprisesperforming a dot product between pixels of an image gradient of theimage of the object of interest and pixels of an outline of a top rim ofthe object of interest in the idealized image.
 12. The method of claim5, wherein the matching of the image of the object of interest to eachidealized image comprises performing a dot product between pixels of animage gradient of the image of the object of interest and pixels of anoutline of a shadow on an inner surface of the object of interest in theidealized image.
 13. The method of claim 5, wherein the matching of theimage of the object of interest to each idealized image comprisessumming: a dot product between pixels of the image of the object ofinterest and pixels of the idealized image; a dot product between pixelsof an image gradient of the image of the object of interest and pixelsof an outline of a top rim of the object of interest in the idealizedimage; and a dot product between pixels of the image gradient and pixelsof an outline of a shadow on an inner surface of the object of interestin the idealized image.
 14. A non-transitory computer-readable storagemedium comprising stored instructions executable by a processor, theinstructions when executed by the processor cause the processor to:extract, from an image of an object of interest, parameters describingat least an azimuth angle of the aerial imaging device; generate, fromthe parameters, idealized images of the object of interest; match theimage of the object of interest to each idealized image to determine afilled volume of the object of interest; and transmit informationcorresponding to the determined filled volume of the object of interestto a user device.
 15. The non-transitory computer-readable storagemedium of claim 14, wherein the instructions to generate each idealizedimage further comprise instructions that when executed cause theprocessor to: generate circles corresponding to a top rim of the objectof interest, an arc of a shadow on an inner surface of the object ofinterest, and a roof of the object of interest; and perform convolutionson the circles.
 16. The non-transitory computer-readable storage mediumof claim 14, wherein the instructions to perform the convolution furthercomprise instructions that when executed cause the processor to performunions and intersections between the three circles.
 17. Thenon-transitory computer-readable storage medium of claim 14, wherein theinstructions to match the image of the object of interest to eachidealized image further comprise instructions that when executed causethe processor to: perform dot products between pixels of the image ofthe object of interest and pixels of the idealized image superimposed atdifferent locations of the image of the object of interest; anddetermine a maximum value of the dot product across the differentlocations.
 18. The non-transitory computer-readable storage medium ofclaim 14, wherein the instructions to match the image of the object ofinterest to each idealized image further comprise instructions that whenexecuted cause the processor to perform a dot product between pixels ofan image gradient of the image of the object of interest and pixels ofan outline of a top rim of the object of interest in the idealizedimage.
 19. The non-transitory computer-readable storage medium of claim14, wherein the instructions to match the image of the object ofinterest to each idealized image further comprise instructions that whenexecuted cause the processor to perform a dot product between pixels ofan image gradient of the image of the object of interest and pixels ofan outline of a shadow on an inner surface of the object of interest inthe idealized image.
 20. The non-transitory computer-readable storagemedium of claim 14, wherein the instructions to match the image of theobject of interest to each idealized image further comprise instructionsthat when executed cause the processor to sum: a dot product betweenpixels of the image of the object of interest and pixels of theidealized image; a dot product between pixels of an image gradient ofthe image of the object of interest and pixels of an outline of a toprim of the object of interest in the idealized image; and a dot productbetween pixels of the image gradient and pixels of an outline of ashadow on an inner surface of the object of interest in the idealizedimage.